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Improving Web Searching Using Descriptive Graphs
Alain CouchotCnam,
Laboratoire Cedric,Equipe Isid
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The web today
Information and services for the user
Excess of information How find the good information ?
Need of information usable by computers
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Semantic web
Semantic annotations Intelligible by the computers
Need of a consensus Communication between distant
computers Addition of a « ontology » layer
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Ontologies Set of objects recognized as
existing Relationships between these
objects Two views :
Universal ontology Ontology depending from the point of
view
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Drawbacks of ontologies Global ontology :
Need of a general consensus Local ontology :
Problem of the inter-ontologies links Problem of the choice of the « good »
ontology for the user
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Simple ontology
Set of concepts Irreflexive, antisymmetric and
transitive relation, noted < Universal concept
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Global terminology Set of simple ontologies If c1 and c2 belong to Oi,
with c1 < c2, then if c1 and c2 belong to Oj,we have c1 < c2, or c1 and c2 are not linked
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Descriptive graphs
Oriented graph built with a simple ontology
A node is labelled by a concept of the simple ontology
A node has one incoming node and one outgoing node
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Precision of a graph Subsumption graph
Subsomption hierarchy Precision of a concept c
Length of the longest path in the subsumption graph from the universal concept to the concept c
Precision of a descriptive graph The greatest precision of the concepts of
the graph
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Example Ontology
Piece of furniture, table, antique dealer, customer, buy, at, (implicit universal concept)
With : table < piece of furniture Graph
customerbuytable atantique dealer Precision of the graph
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Average and significant precisions Average precision of a concept
Average of the precisions of the concept for all the ontologies of the terminology
Significant precision of a graph Average of the average precisions of
the concepts of the graph
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Example Ontology O1
Piece of furniture, table, antique dealer, customer
With table < piece of furniture Ontology O2
table, antique dealer, customer Average precision of « table »
(3+2)/2 = 2.5
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Composite antecedent Precision k antecedent of a
concept Hypernym concept whose precision is
k It is possible to prove that there is
always a precision k antecedent Composite precision k antecedent
Conjunction of all the precision k antecedents
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Example Ontology
Graduate student, student, teacher With : graduate student < student
and graduate student < teacher Precision 2 composite antecedent
of graduate student student AND teacher
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Partial identity Partial identity of two composite
antecedents A and B A = a1 AND a2 AND … AND am B = b1 AND b2 AND … AND bn A and B partieallly identical if there is
i, j / ai = bj Example
A =land-vehicle AND amphibian-vehicle
B = land-vehicle AND flying-vehicle
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View of a graph at the level k A concept whose précision is > k is
replaced by its composite precision k antecedent
Notation : V(G, k) Two views V1 = C1C2…Cn and
V2 = D1 D2…Dp are identical if n = p and if the composite concepts Ci and Di are partially identical
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Example Ontology
Piece of furniture, table, antique dealer, customer, buy, at
With: table < piece of furniture Graph G
customerbuytable atantique dealer V(G,2)
customerbuypiece of furniture atantique dealer
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Similarity of two graphs We determine k1 and k2 such as
V(G1, k1) and V(G2, k2) are identical V(G1, k1+1) and V(G2, k2) are not
identical V(G1, k1) and V(G2, k2+1) are not
identical Similarity coefficient
(Sign_Prec(V(G1,k1))+Sign_Prec(V(G2, k2))) / (Sign_Prec(G1)+Sign_Prec(G2))
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Example Ontology O1
customer, antique dealer, buy, at, piece of furniture, table, leg, decoration, good, seller
With: leg < table < piece of furniture and piece of furniture < good and antique dealer < seller
Ontology O2 customer, seller, bibelot, decoration, buy, at With: bibelot < decoration
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Example Graph G1 built with O1
customerbuylegatantique dealer V(G1,4)
customerbuytableatantique dealer V(G1,3)
customerbuypiece of furnitureatantique dealer
V(G1,2) customerbuydecorationatseller
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Example Graph G2 built with O2
customerbuybibelotatseller V(G2,2)
customerbuydecorationatseller V(G1,2) and V(G2,2) are identical Similarity coefficient
(2 + 2) / (2.8 + 2.2) = 0.8
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Conclusion Global terminology and simple
ontologies Descriptive graphs View of a graph Similarity coefficient Future work
Automatic buildong of the descriptive graphs associated to the web ressources
Specifcation of the queries using the natural language